Characterization of four subtypes in morphologically normal tissue excised proximal and distal to breast cancer

Widespread mammographic screening programs and improved self-monitoring allow for breast cancer to be detected earlier than ever before. Breast-conserving surgery is a successful treatment for select women. However, up to 40% of women develop local recurrence after surgery despite apparently tumor-free margins. This suggests that morphologically normal breast may harbor early alterations that contribute to increased risk of cancer recurrence. We conducted a comprehensive transcriptomic and proteomic analysis to characterize 57 fresh-frozen tissues from breast cancers and matched histologically normal tissues resected proximal to (<2 cm) and distant from (5–10 cm) the primary tumor, using tissues from cosmetic reduction mammoplasties as baseline. Four distinct transcriptomic subtypes are identified within matched normal tissues: metabolic; immune; matrisome/epithelial–mesenchymal transition, and non-coding enriched. Key components of the subtypes are supported by proteomic and tissue composition analyses. We find that the metabolic subtype is associated with poor prognosis (p < 0.001, HR6.1). Examination of genes representing the metabolic signature identifies several genes able to prognosticate outcome from histologically normal tissues. A subset of these have been reported for their predictive ability in cancer but, to the best of our knowledge, these have not been reported altered in matched normal tissues. This study takes an important first step toward characterizing matched normal tissues resected at pre-defined margins from the primary tumor. Unlocking the predictive potential of unexcised tissue could prove key to driving the realization of personalized medicine for breast cancer patients, allowing for more biologically-driven analyses of tissue margins than morphology alone

Evaluation of a Coupled Model to Predict the Impact of Adaptive Behaviour in the Thermal Sensation of Occupants of Naturally Ventilated Buildings in Warm-Humid Regions

Improving indoor environment quality and making urban centres in tropical regions more sustainable has become a challenge for which computational models for the prediction of thermal sensation for naturally ventilated buildings (NVBs) have major role to play. This work performed analysis on thermal sensation for non-residential NVBs located in Brazilian tropical warm-humid climate and tested the effectiveness of suggested adaptive behaviours to mitigate warm thermal sensation. The research method utilized transient computational fluid dynamics models coupled with a dynamic model for human thermophysiology to predict thermal sensation. The calculated results were validated with comparison with benchmark values from questionnaires and from field measurements. The calculated results for dynamic thermal sensation (DTS) seven-point scale showed higher agreement with the thermal sensation vote than with the predicted mean vote. The test for the suggested adaptive behaviours considered reducing clothing insulation values from 0.18 to 0.32 clo (reducing DTS from 0.1 to 0.9), increasing the air speed in 0.9 m/s (reducing DTS from 0.1 to 0.9), and applying both suggestions together (reducing DTS from 0.1 to 1.3) for five scenarios with operative temperatures spanning 34.5&ndash;24.0 &deg;C. Results quantified the tested adaptive behaviours&rsquo; efficiency showing applicability to improve thermal sensation from slightly-warm to neutral

Prediction of the Impact of Air Speed Produced by a Mechanical Fan and Operative Temperature on the Thermal Sensation

Natural ventilation associated with a mechanical fan is a feasible strategy to enhance thermal acceptability in warm weather. The ASHRAE-55 provides the increase for operative temperature proportional to the increase in air speed while maintaining thermal comfort. Conversely, the range of informed values is limited and little guidance for mechanical fans is provided. This work explores the relationship between operative temperature and air speed produced by ceiling fans, and the effective-ness to deliver thermal comfort for a wider range of values. The research method comprises transient computer fluid dynamics simulations coupled with a thermal sensation model and is divided into two stages: a calibration exercise and a parametrical investigation. Three matrices are presented for a range of operative temperatures (21.0–36.0◦ C) and air speeds (0–2.5 m/s) for: Dynamic Thermal Sensation (DTS) (a computer-based seven-point index), Predicted Percentage of Dissatisfied, and potential Cooling Effect. When compared to the Predicted Mean Vote, the DTS overestimates thermal comfort for temperatures under 28.0◦ C with increased air speed and overestimates discomfort for temperatures above 31.0◦ C, even with increased air speed. Agreement is found between both scales for 28.0–31.0◦ C, defining a range for the effective use of ceiling fans to provide thermal comfort under warm weather conditions. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Semantic similarity-driven decision support in the skeletal dysplasia domain

Biomedical ontologies have become a mainstream topic in medical research. They represent important sources of evolved knowledge that may be automatically integrated in decision support methods. Grounding clinical and radiographic findings in concepts defined by a biomedical ontology, e.g., the Human Phenotype Ontology, enables us to compute semantic similarity between them. In this paper, we focus on using such similarity measures to predict disorders on undiagnosed patient cases in the bone dysplasia domain. Different methods for computing the semantic similarity have been implemented. All methods have been evaluated based on their support in achieving a higher prediction accuracy. The outcome of this research enables us to understand the feasibility of developing decision support methods based on ontology-driven semantic similarity in the skeletal dysplasia domain